1. Sampling
And
Sampling Methods

2. INTRODUCTION
In all spheres of life the need for statistical investigation and data analysis is rising day by day. There are two methods of collection of data: (i) CENSUS METHOD and (ii) SAMPLE METHOD . Under census method information relating to entire field of investigation or units of population is collected , where as under sample method, rather than collecting information about all the units of population, information relating to only selected units is collected.

3. Sampling Concepts
Population/Target population: This is any complete, or the theoretically specified aggregation of study elements. It is usually the ideal population or universe to which research results are to be generalized. For example, all adult population of the U.S.
Sample: In statistics, a sample is a subset of a population. Typically, the population is very large, making a census or a complete enumeration of all the values in the population impractical or impossible. The sample represents a subset of manageable size. Samples are collected and statistics are calculated from the samples so that one can make inferences or extrapolations from the sample to the population. This process of collecting information from a sample is referred to as sampling.

4. What exactly IS a “sample”?

5. CENSUS METHOD
A census is the procedure of systematically acquiring and recording information about the members of a given population. It is a regularly occurring and official count of a particular population. The term is used mostly in connection with national population and housing censuses; other common censuses include agriculture, business, and traffic censuses. In the latter cases the elements of the 'population' are farms, businesses, and so forth, rather than people. This method is also known as Complete Enumeration Method

6. SAMPLING METHOD
In statistics , sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster.
Each observation measures one or more properties (such as weight, location, color) of observable bodies distinguished as independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly stratified sampling (blocking). Results from probability theory and statistical theory are employed to guide practice. In business and medical research, sampling is widely used for gathering information about a population.

7. Types of samples

8. Simple Random Sample Get a list or “sampling frame”
This is the hard part! It must not systematically exclude anyone.
Generate random numbers
Select one person per random
numbers

9. Systematic Random Sample
Select a random number, which will be known as k
Get a list of people, or observe a flow of people (e.g., pedestrians on a corner)
Select every kth person
Careful that there is no systematic rhythm to the flow or list of people.
If every 4th person on the list is, say, “rich” or “senior” or some other consistent pattern, avoid this method

10. Stratified Random Sample
Separate your population into groups or “strata”
Do either a simple random sample or systematic random sample from there
Note you must know easily what the “strata” are before attempting this
If your sampling frame is sorted by, say, school district, then you’re able to use this method

11. Multi-stage Cluster Sample
Get a list of “clusters,” e.g., branches of a company
Randomly sample clusters from that list
Have a list of, say, 10 branches
Randomly sample people within those branches
This method is complex and expensive

12. The Convenience Sample
Find some people that are easy to find

13. The Snowball Sample Find a few people that are relevant to your topic.
Ask them to refer you to more of them.

14. The Quota Sample
Determine what the population looks like in terms of specific qualities.
Create “quotas” based on those qualities.
Select people for each quota.

15. Accidental sampling
A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand
The researcher using such a sample cannot scientifically make generalizations about the total population 
In social science research, snowball sampling is a similar technique

16. Panel sampling
The method of first selecting a group of participants through a random sampling
Period of data collection is called a "wave“
Panel sampling can also be used to inform researchers about within-person health changes due to age

17. Sampling Errors
  These are the errors which occur due to the nature of sampling. The sample selected from the population is one of all possible samples. Any value calculated from the sample is based on the sample data and is called sample statistic. The sample statistic may or may not be close to the population parameter. If the statistic is  and the true value of the population parameter is, then the difference is called sampling error. It is important to note that a statistic is a random variable and it may take any value. A particular example of sampling error is the difference between the sample mean and the population mean. Thus sampling error is also a random term.

18. Reducing the Sampling Errors:
By increasing the size of the sample.
By Stratification.

19. Non sampling errors
A statistical error caused by human error to which a specific statistical analysis is exposed. These errors can include, but are not limited to, data entry errors, biased questions in a questionnaire, biased processing/decision making, inappropriate analysis conclusions and false information provided by respondents.
Some are following:
Faulty plaining
Faulty selection of sample units
Errors in compilation
Framing of wrong questionnaire